Physical properties of electron beam irradiated poly(vinyl butyral) composites with carbamate,imidazole, and tetrazolium dye

Films of poly(vinyl butyral) composites with triphenyl tetrazolium chloride dye (TTC) and the antioxidant nickel dibutyl dithiocarbamate (NBC) or 2‐mercaptobenzimidazole (MBI) were prepared by the solution casting method using butyl alcohol as a solvent. The effect of various doses of electron beam irradiation (50–200 kGy) on color response, thermal, and mechanical properties were investigated. The color measurements showed that the films of the different composites possessed a high sensitivity to electron beam irradiation, in which the nearly colorless films were changed to deep red color, which can be easily detected by visual observations. In addition, the change in color depends on irradiation dose and the contents of the TTC dye. Moreover, the presence of the antioxidants NBC or MBI has no effect on the development of color. However, PVB/TTC and PVB/TTC/MBI composites showed high regular change in color as a function of irradiation dose. The thermogravimetric analysis used to study the thermal stability indicated that PVB/TTC composites either before or after electron beam irradiation are thermally more stable than neat PVB polymer. The presence of the antioxidants NBC or MBI offered protection to PVB/TTC composites against decomposition or oxidative degradation resulted from irradiation. Blending unirradiated PVB with TTC dye, mixture of TTC and NBC, and mixture of TTC and MBI reduced the tensile strength by 4, 20, and 17% upon blending, respectively. However, the reduction in tensile strength of the entire composite films (～7% based on the initial value) upon exposure to a dose of 50 kGy is acceptable for practical applications. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4358–4365, 2006     

Biodegradation of low‐density polyethylene/thermoplastic starch foams before and after electron beam irradiation

Foamy low‐density polyethylene/plasticized starch (LDPE/PLST) blends at different compositions were produced in the presence of azodicarbonamide (ACA) compound as foaming agent. The LDPE/PLST blends before and after electron beam irradiation were investigated in terms of mechanical properties, bulk density, and structure morphology. Moreover, the biodegradability of these materials was evaluated by the soil burial test for 2 months, in which the buried sheets were also examined by scanning electron microscopy (SEM). The results showed that the increase of PLST content from 24 to 30% was accompanied by a decrease in the yield and break stresses of 10 and 20% for the unirradiated blends without the foaming agent, respectively. Further decrease in these mechanical parameters was observed after the foaming process. The bulk density, void fraction, cell size measurements as well as the examination by SEM illustrate clearly the cell growth of the foam structure. The soil burial test and SEM micrographs indicate the growth of microorganisms overall the blend sheets and that the blend was completely damaged after two months of burying. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Evaluation of rutting potential for asphalt concrete mixes containing copper slag

Copper slag (CS) is a by-product of the copper extraction process, which can be used as coarse and/or fine aggregate in hot mix asphalt (HMA) pavements. This study used CS as a replacement of the fine aggregate with a percentage of up to 40% by total aggregate weight. The objective of this study was to evaluate the effect of CS on the rutting potential of the asphalt concrete mix using two methods. One method is based on the Dynamic modulus |E*| testing result. Actual pavement temperature data from a test section were used with the developed |E*| master curves. EverStressFE finite element program was used to perform a linear elastic load-deformation analysis for a pavement section and to determine the vertical resilient strain in a 40-mm HMA surface layer. The M-E PDG permanent deformation model was used with and Excel Visual Basic for Applications code to predict the accumulated rutting for different CS mixes for 10 million ESALs. The other method used the data from the flow number (FN) test. Based on the |E*| approach, the results indicated that adding 5% CS in the mix increased the predicted rutting from 0.59 to 0.98 mm at 10 million ESALs (increase by 68%). When 40% CS was used, rutting increased by more than 700% compared with the control mix. After analysing the FN results with the Francken model, the results indicated a decrease in FN as CS content is increased, indicating higher rutting potential. The decrease in FN ranged from 9% for 5% CS to 95% for 40% CS. The mixes containing up to 10% CS satisfied the minimum FN criteria for rutting. A calibration process for the M-E PDG distress prediction models that allows the use of waste and by-product materials such as CS should be considered in the future.     

Congestion prevention in broadband wireless access systems: An economic approach

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.     

Implementation of speed controller for rotary hydraulic motor based on LS-SVM

The performance of a model-based control system depends strongly on the accuracy of the process model used. LS-SVM is a powerful method for modeling nonlinear systems. The main objective of this paper is to implement a conventional controller based on LS-SVM model for hydraulic motor. An off-line model is first identified based on LS-SVM, then via simulation tests the parameters of the discrete PI-Controller and its velocity-form are obtained then the controller parameters are applied experimentally for the hydraulic motor as a speed controller. The system performance has been evaluated; results show good performance over a wide range of operating conditions and load disturbances.     

Curve/surface representation and evolution using vector level sets with application to the shape-based segmentation problem

In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in (H. E. Abd El Munim, et al., Oct. 2005). Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework     

Precipitation Behavior of Modified as Cast High Nitrogen Super Hard High‐Speed Tool Steel

The properties of high‐speed tool steels can be improved by modifying their chemical composition or the technology of production. Nitrogen alloying is an attractive technology to enhance the mechanical and physical properties of tool steels. In this work, modified super hard high‐speed tool steel was produced through nitrogen alloying and decreasing the level of cobalt content in investigated steels. This work aims to study the effect of nitrogen as alloying element on carbides and carbo‐nitrides precipitates type, shape, and size for investigated steels. From the results obtained from Thermo‐Calc, it was concluded that nitrogen alloying produced large amount of stable austenite, also eutectic carbides precipitates (M₆C and M₇C₃) were stable at room temperature. Transmission electron microscope (TEM) images for traditional grade showed that the as cast structure contains beside the carbides network other single carbides precipitates. While on the other hand the selected area diffraction pattern (SADP) images of nitrogen alloyed grade shows fine carbides and carbo‐nitrides precipitates with more amount of retained austenite in the ferrite matrix, they showed also the presence of the eutectic precipitates as well as the dislocations.     

A multi-variate heart disease optimization and recognition framework

Neural Computing and Applications - Cardiovascular diseases (CVD) are the most widely spread diseases all over the world among the common chronic diseases. CVD represents one of the main causes of...     

Performance optimization of integrated gas and power within microgrids using hybrid PSO–PS algorithm

In this paper, a hybrid algorithm consisting of particle swarm optimization and pattern search algorithm is proposed to evaluate and optimize the design and operation of microgrids (MGs) in combined gas and power networks. Key performance indicators (KPIs) are modeled and proposed to evaluate and assess MGs. The paper begins by proposing a comprehensive study to define KPIs, which are used to evaluate the following MG parameters: economical efficiency, reliability, environmental conservation, and power quality. Multi‐objective evaluation functions are then developed by building a relationship matrix of MG and KPI components. The results are then displayed as optimized power generation percentages for each technology with values for four KPI categories: cost, quality, reliability and environmental friendliness. Two case studies are examined in this paper; both the province of Ontario and Toronto regional zone under all system parameters with varying percentage of generation via gas technology. Results indicated that the optimal scenario for both Ontario and Toronto was achieved at hybrid PSO–patern search percentage generation via gas technology with improved cost KPI and other KPIs remaining approximately constant. Copyright © 2016 John Wiley & Sons, Ltd.